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# Counting duplicate rows in Pandas DataFrame

schedule Aug 10, 2023
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To count the number of duplicate rows, use the DataFrame's `duplicated(~)` method.

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,3],"B":[5,6,5]}, index=["a","b","c"])df A Ba 3 5b 4 6c 3 5 ```

Here, rows `a` and `c` are duplicates.

# Solution

To get the number of duplicate rows:

``` df.duplicated(keep=False).sum() 2 ```

# Explanation

Here, the `duplicated(~)` method returns a Series of booleans where the duplicate rows are marked as `True`:

``` df.duplicated(keep=False) a Trueb Falsec Truedtype: bool ```

The `keep=False` indicates that we want all the duplicate rows to be marked as `True`, as opposed to only the `"first"` or `"last"`.

Recall that `True` is represented internally as `1`, and `False` as `0`, so summing up our Series would return the total number of duplicate rows in the DataFrame.

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